Utilizing AI-Powered Thematic Analysis: Methodology, Implementation, and Lessons Learned

Abstract

Artificial intelligence (AI) has the potential to transform healthcare, medical education, and research. Large language models (LLMs) have gained attention for their ability to improve qualitative research by automating data analysis, coding, and thematic interpretation. While prior research evaluates LLMs' performance in qualitative studies, clear guidelines on their implementation remain scarce. This manuscript offers detailed methods with instructions and prompts for using LLMs in qualitative analysis. It provides a clear, step-by-step, practical approach. We developed a customized generative pre-trained transformer (Custom-GPT) based on Braun and Clarke's six-step thematic analysis framework. The performance of the model was evaluated across three datasets, comparing its outputs with manually generated codes and themes. Triangulation was conducted using Google's NotebookLM. Across the three datasets, the model generated consistent thematic structures that aligned closely with manual coding. However, slight variability in responses, lack of AI decision-making explanations, and requiring repeated prompting during the process were the main challenges. Additional human interventions were required between steps to refine outputs and ensure methodological integrity. LLMs offer promising opportunities to enhance qualitative thematic analysis. However, their limitations emphasize the necessity of human oversight throughout the process. This report highlights the importance of integrating AI tools responsibly, emphasizing methodological rigor, and developing clear guidelines for AI-assisted qualitative research. Future research should explore ethical frameworks, domain-specific LLMs, and advanced prompt engineering techniques to optimize AI's role in qualitative analysis.

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